Purpose: Personalized medicine is expected to yield improved health outcomes. Data mining over massive volumes of patients' clinical data is an appealing, low-cost and noninvasive approach toward personalization. Machine learning algorithms could be trained over clinical "big data" to build prediction models for personalized therapy. To reach this goal, a scalable "big data" architecture for the medical domain becomes essential, based on data standardization to transform clinical data into FAIR (Findable, Accessible, Interoperable and Reusable) data. Using Ontologies and Semantic Web technologies, we attempt to reach mentioned goal.
Methods: We developed an ontology to be used in the field of radiation oncology to map clinical data from relational databases. We combined ontology with semantic Web techniques to publish mapped data and easily query them using SPARQL.
Results: The Radiation Oncology Ontology (ROO) contains 1,183 classes and 211 properties between classes to represent clinical data (and their relationships) in the radiation oncology domain following FAIR principles. We combined the ontology with Semantic Web technologies showing how to efficiently and easily integrate and query data from different (relational database) sources without a priori knowledge of their structures.
Discussion: When clinical FAIR data sources are combined (linked data) using mentioned technologies, new relationships between entities are created and discovered, representing a dynamic body of knowledge that is continuously accessible and increasing.
Keywords: ontologies; radiation oncology; semantic web.
© 2018 American Association of Physicists in Medicine.